tar_visnetwork: visNetwork dependency graph.
In targets: Dynamic Function-Oriented 'Make'-Like Declarative Pipelines

tar_visnetwork

R Documentation

visNetwork dependency graph.

Description

Visualize the pipeline dependency graph with a visNetwork HTML widget.

Usage

tar_visnetwork(
  targets_only = FALSE,
  names = NULL,
  shortcut = FALSE,
  allow = NULL,
  exclude = ".Random.seed",
  outdated = TRUE,
  label = targets::tar_config_get("label"),
  label_width = targets::tar_config_get("label_width"),
  level_separation = targets::tar_config_get("level_separation"),
  degree_from = 1L,
  degree_to = 1L,
  zoom_speed = 1,
  physics = FALSE,
  reporter = targets::tar_config_get("reporter_outdated"),
  seconds_reporter = targets::tar_config_get("seconds_reporter"),
  callr_function = callr::r,
  callr_arguments = targets::tar_callr_args_default(callr_function),
  envir = parent.frame(),
  script = targets::tar_config_get("script"),
  store = targets::tar_config_get("store")
)

Arguments

`targets_only`	Logical, whether to restrict the output to just targets (`FALSE`) or to also include global functions and objects.
`names`	Names of targets. The graph visualization will operate only on these targets (and unless `shortcut` is `TRUE`, all the targets upstream as well). Selecting a small subgraph using `names` could speed up the load time of the visualization. Unlike `allow`, `names` is invoked before the graph is generated. Set to NULL to check/run all the targets (default). Otherwise, the object supplied to `names` should be a `tidyselect` expression like `any_of()` or `starts_with()` from `tidyselect` itself, or `tar_described_as()` to select target names based on their descriptions.
`shortcut`	Logical of length 1, how to interpret the `names` argument. If `shortcut` is `FALSE` (default) then the function checks all targets upstream of `names` as far back as the dependency graph goes. If `TRUE`, then the function only checks the targets in `names` and uses stored metadata for information about upstream dependencies as needed. `shortcut = TRUE` increases speed if there are a lot of up-to-date targets, but it assumes all the dependencies are up to date, so please use with caution. Also, `shortcut = TRUE` only works if you set `names`.
`allow`	Optional, define the set of allowable vertices in the graph. Unlike `names`, `allow` is invoked only after the graph is mostly resolved, so it will not speed up execution. Set to `NULL` to allow all vertices in the pipeline and environment (default). Otherwise, you can supply symbols or `tidyselect` helpers like `starts_with()`.
`exclude`	Optional, define the set of exclude vertices from the graph. Unlike `names`, `exclude` is invoked only after the graph is mostly resolved, so it will not speed up execution. Set to `NULL` to exclude no vertices. Otherwise, you can supply symbols or `tidyselect` helpers like `any_of()` and `starts_with()`.
`outdated`	Logical, whether to show colors to distinguish outdated targets from up-to-date targets. (Global functions and objects still show these colors.) Looking for outdated targets takes a lot of time for large pipelines with lots of branches, and setting `outdated` to `FALSE` is a nice way to speed up the graph if you only want to see dependency relationships and pipeline progress.
`label`	Character vector of one or more aesthetics to add to the vertex labels. Can contain `"description"` to show each target's custom description, `"time"` to show total runtime, `"size"` to show total storage size, or `"branches"` to show the number of branches in each pattern. You can choose multiple aesthetics at once, e.g. `label = c("description", "time")`. Only the description is enabled by default.
`label_width`	Positive numeric of length 1, maximum width (in number of characters) of the node labels.
`level_separation`	Numeric of length 1, `levelSeparation` argument of `visNetwork::visHierarchicalLayout()`. Controls the distance between hierarchical levels. Consider changing the value if the aspect ratio of the graph is far from 1. If `level_separation` is `NULL`, the `levelSeparation` argument of `visHierarchicalLayout()` defaults to `150`.
`degree_from`	Integer of length 1. When you click on a node, the graph highlights a neighborhood of that node. `degree_from` controls the number of edges the neighborhood extends upstream.
`degree_to`	Integer of length 1. When you click on a node, the graph highlights a neighborhood of that node. `degree_to` controls the number of edges the neighborhood extends downstream.
`zoom_speed`	Positive numeric of length 1, scaling factor on the zoom speed. Above 1 zooms faster than default, below 1 zooms lower than default.
`physics`	Logical of length 1, whether to implement interactive physics in the graph, e.g. edge elasticity.
`reporter`	Character of length 1, name of the reporter to user. Controls how messages are printed as targets are checked. Choices: `"silent"`: print nothing. `"forecast"`: print running totals of the checked and outdated targets found so far.
`seconds_reporter`	Positive numeric of length 1 with the minimum number of seconds between times when the reporter prints progress messages to the R console.
`callr_function`	A function from `callr` to start a fresh clean R process to do the work. Set to `NULL` to run in the current session instead of an external process (but restart your R session just before you do in order to clear debris out of the global environment). `callr_function` needs to be `NULL` for interactive debugging, e.g. `tar_option_set(debug = "your_target")`. However, `callr_function` should not be `NULL` for serious reproducible work.
`callr_arguments`	A list of arguments to `callr_function`.
`envir`	An environment, where to run the target R script (default: `⁠_targets.R⁠`) if `callr_function` is `NULL`. Ignored if `callr_function` is anything other than `NULL`. `callr_function` should only be `NULL` for debugging and testing purposes, not for serious runs of a pipeline, etc. The `envir` argument of `tar_make()` and related functions always overrides the current value of `tar_option_get("envir")` in the current R session just before running the target script file, so whenever you need to set an alternative `envir`, you should always set it with `tar_option_set()` from within the target script file. In other words, if you call `tar_option_set(envir = envir1)` in an interactive session and then `tar_make(envir = envir2, callr_function = NULL)`, then `envir2` will be used.
`script`	Character of length 1, path to the target script file. Defaults to `tar_config_get("script")`, which in turn defaults to `⁠_targets.R⁠`. When you set this argument, the value of `tar_config_get("script")` is temporarily changed for the current function call. See `tar_script()`, `tar_config_get()`, and `tar_config_set()` for details about the target script file and how to set it persistently for a project.
`store`	Character of length 1, path to the `targets` data store. Defaults to `tar_config_get("store")`, which in turn defaults to `⁠_targets/⁠`. When you set this argument, the value of `tar_config_get("store")` is temporarily changed for the current function call. See `tar_config_get()` and `tar_config_set()` for details about how to set the data store path persistently for a project.

Value

A visNetwork HTML widget object.

Dependency graph

The dependency graph of a pipeline is a directed acyclic graph (DAG) where each node indicates a target or global object and each directed edge indicates where a downstream node depends on an upstream node. The DAG is not always a tree, but it never contains a cycle because no target is allowed to directly or indirectly depend on itself. The dependency graph should show a natural progression of work from left to right. targets uses static code analysis to create the graph, so the order of tar_target() calls in the ⁠_targets.R⁠ file does not matter. However, targets does not support self-referential loops or other cycles. For more information on the dependency graph, please read https://books.ropensci.org/targets/targets.html#dependencies.

Storage access

Several functions like tar_make(), tar_read(), tar_load(), tar_meta(), and tar_progress() read or modify the local data store of the pipeline. The local data store is in flux while a pipeline is running, and depending on how distributed computing or cloud computing is set up, not all targets can even reach it. So please do not call these functions from inside a target as part of a running pipeline. The only exception is literate programming target factories in the tarchetypes package such as tar_render() and tar_quarto().

Examples

if (identical(Sys.getenv("TAR_INTERACTIVE_EXAMPLES"), "true")) {
tar_dir({ # tar_dir() runs code from a temp dir for CRAN.
tar_script({
  library(targets)
  library(tarchetypes)
  tar_option_set()
  list(
    tar_target(y1, 1 + 1),
    tar_target(y2, 1 + 1),
    tar_target(z, y1 + y2, description = "sum of two other sums")
  )
})
tar_visnetwork()
tar_visnetwork(allow = starts_with("y")) # see also any_of()
})
}

targets documentation built on Oct. 3, 2024, 1:11 a.m.